3 citations · 17 across the 25 of their papers we have counts for
21 papers · 1 filter
One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
Bowen He, Juncheng Dong, Lin Lin +1
A central challenge in reinforcement learning (RL) is to learn models that generalize beyond the tasks on which they are trained, a goal traditionally pursued through multi-task an…
Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
Juncheng Dong, Bowen He, Moyang Guo +3
In-context reinforcement learning (ICRL) leverages the in-context learning capabilities of transformer models (TMs) to efficiently generalize to unseen sequential decision-making t…
In-Context Reinforcement Learning From Suboptimal Historical Data
Juncheng Dong, Moyang Guo, Ethan X. Fang +2
Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training an autoregressive tran…
CARE: Turning LLMs Into Causal Reasoning Expert
Juncheng Dong, Yiling Liu, Ahmed Aloui +2
Large language models (LLMs) have recently demonstrated impressive capabilities across a range of reasoning and generation tasks. However, research studies have shown that LLMs lac…
Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimation
Yiling Liu, Juncheng Dong, Chen Fu +4
Estimating counterfactual outcomes from time-series observations is crucial for effective decision-making, e.g. when to administer a life-saving treatment, yet remains significantl…
PASTA: A Unified Framework for Offline Assortment Learning
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We study a broad class of assortment optimization problems in an offline and data-driven setting. In such problems, a firm lacks prior knowledge of the underlying choice model, and…
Conditional Average Treatment Effect Estimation Under Hidden Confounders
Ahmed Aloui, Juncheng Dong, Ali Hasan +1
One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for h…
Teleportation With Null Space Gradient Projection for Optimization Acceleration
Zihao Wu, Juncheng Dong, Ahmed Aloui +1
Optimization techniques have become increasingly critical due to the ever-growing model complexity and data scale. In particular, teleportation has emerged as a promising approach,…
S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting
Zihao Wu, Juncheng Dong, Haoming Yang +1
Time series forecasting has recently achieved significant progress with multi-scale models to address the heterogeneity between long and short range patterns. Despite their state-o…
Score-Based Metropolis-Hastings Algorithms
Ahmed Aloui, Ali Hasan, Juncheng Dong +2
In this paper, we introduce a new approach for integrating score-based models with the Metropolis-Hastings algorithm. While traditional score-based diffusion models excel in accura…
Offline Stochastic Optimization of Black-Box Objective Functions
Juncheng Dong, Zihao Wu, Hamid Jafarkhani +2
Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search…
CATE Estimation With Potential Outcome Imputation From Local Regression
Ahmed Aloui, Juncheng Dong, Cat P. Le +1
One of the most significant challenges in Conditional Average Treatment Effect (CATE) estimation is the statistical discrepancy between distinct treatment groups. To address this i…
Off-Policy Evaluation for Human Feedback
Qitong Gao, Ge Gao, Juncheng Dong +3
Off-policy evaluation (OPE) is important for closing the gap between offline training and evaluation of reinforcement learning (RL), by estimating performance and/or rank of target…
Robust Reinforcement Learning through Efficient Adversarial Herding
Juncheng Dong, Hao-Lun Hsu, Qitong Gao +2
Although reinforcement learning (RL) is considered the gold standard for policy design, it may not always provide a robust solution in various scenarios. This can result in severe…
Mode-Aware Continual Learning for Conditional Generative Adversarial Networks
Cat P. Le, Juncheng Dong, Ahmed Aloui +1
The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end,…
PASTA: Pessimistic Assortment Optimization
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline da…
Domain Adaptation via Rebalanced Sub-domain Alignment
Yiling Liu, Juncheng Dong, Ziyang Jiang +5
Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a different but related unlabeled target domain. While many UDA metho…
Transfer Learning for Individual Treatment Effect Estimation
Ahmed Aloui, Juncheng Dong, Cat P. Le +1
This work considers the problem of transferring causal knowledge between tasks for Individual Treatment Effect (ITE) estimation. To this end, we theoretically assess the feasibilit…
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Juncheng Dong, Simiao Ren, Yang Deng +5
Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In ma…
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
Cat P. Le, Juncheng Dong, Mohammadreza Soltani +1
We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartit…
Fisher Task Distance and Its Application in Neural Architecture Search
Cat P. Le, Mohammadreza Soltani, Juncheng Dong +1
We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices, called Fisher task distance. This distance represents the complexity of…